One effective and efficient method to reduce model complexity is through network pruning. The primary goal of pruning is to remove the parameters, along with their computation, that are deemed least important for inference [1]}, [2]}, [3]}, [4]}. Compatible with other compression streams of work such as quantization [5]}, [6]}, [7]} and distillation [8]}, [9]}, [10]}, network pruning enables a flexible tuning of model complexity towards varying constraints, while requiring much less design efforts by neural architecture search [11]}, [12]}, [13]} and architecture re-designs [14]}, [15]}, [16]}. Thus, in this work, we study pruning, in particular structured pruning that removes filters (or neurons) to benefit off-the-shelf platforms, e.g., GPUs.
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